Before the lesson:
Please make sure you got the latest RStudio and latest R version installed.

Lesson objectives:
* learn to perform a search in academic literature database
* download search results and import them into R
* summarise bibliometric data
* make a few types of simple bibliometric networks
* plot bibliometric networks

Lesson outline:
* About this lesson
* Getting bibliometric data
* Summarising bibliometric data
* Creating bibliometric networks
* More resouces

About this lesson

This lesson is prepared for these who are already familiar with R coding language, R markdown and RStudio. By the end of this tutorial you should be able to create a simple html document containing markdown-formatted text, images and R code, all in R Studio.

Getting bibliometric data

You can do analyses of literature on any topic. In this lesson we will have a look at the academic literature related to the concept of Terminal Investment. Terminal Investment hypothesis predicts increased investment of resources into reproduction as the chances of survival decrease. This can be observed as increased reproductive effort in older animals or in animals challenged with factors signalling threat to their survival (e.g., predation, pathogenes, parasites).

Terminal investment in animals is usually studied in three main ways:
1. via observational studies of correlations of age and reproductive effort,
2. in experimantal studies where animals are subject to immune challenges and their subsequent reprodactive effort is compared to unchallenged aninmals of the same age,
3. in experimantal studies where reproductive response to immune challenge is compared between animals of older ages versus younger ages.

You can read more on this Wiki page: https://en.wikipedia.org/wiki/Terminal_investment_hypothesis

We hope the topic is quite appealing and quiet easy to understand.There are several published reviews on terminal investment hypothesis and we can expect many publications related to this topic, as well as many researchers working on it. Is this so?

Thus, we will try to run bibliometric analyses on the relevant sample of literature. Note that some R packages (and many other online/software tools) are available (and more are being developed) that can perform some of the tasks which we will practice during this exercise, and often much more. For your own project you may want to try to use some of them, but there is no single “perfect” tool that fits all possible analyses and that is easy and usable for all disciplines and types of research questions. Note that the main purpose of this exercise is to familiarize you with the basic principles/issues of bibliometric analyses. You can always learn more in your own time if you are interested.

The search

First, we need to find a representative sample of academic publications on our topic of choice. For this, we will use cross-disciplianary database of academic literature, Scopus. This database has the largest coverage of the published literature and should give us the most complete picture.
Note that we have free access to this database on campus, but you will not be able to access it from outside the campus unless (you use UNSW or other university proxy servers). An alternative database, commonly used for broad academic literature searches and analyses, is Web of Science: https://www.webofknowledge.com/.

TASK 1

Go to Scopus search page: https://www.scopus.com/search/form.uri?display=basic and enter “terminal investment” (with the quotes) in the basic search window, as follows:

Press “Search”" button. You shoud see something like this:

Hey, this does not look good… - very few documents were found and some of them are completely unrelated (building shipping terminals).
Why is that?

This is because our search is too simple. It allows us only to find the papers that explicitly mention “terminal investment” phrase in their title, abstract or keywords. To find a better set of papers for the analyses, we need a more sophisticated search string. Additionally, we will focus our topic a little bit more and aim to find papers that use immune challenge approach in wild or semi-wild animal species (so, we try to exclude established lab model species such as mice and rats, domesticated animals such as dogs and pigs, and humans). Finding the best search string is a bit of an art, so we just provide you with this one to save time:

(TITLE-ABS-KEY ( ( "terminal investment"  OR  "reproductive effort"  OR  "fecundity compensation"  OR  "reproductive compensation"  OR  "reproductive fitness"  OR  "reproductive investment"  OR  "reproductive success"  OR  "Life History Trade-Off*"  OR  "Phenotypic Plasticity" )  AND  ( "immune challeng*"  OR  "immunochalleng*"  OR  "infect*"  OR  lipopolysaccharide  OR  lps  OR  phytohemagglutinin  OR  pha  OR  "sheep red blood cells"  OR  srbc  OR  implant  OR  vaccin* ) )  AND NOT  TITLE-ABS-KEY ( load  OR  human  OR  people  OR  men  OR  women  OR  infant*  OR  rat  OR  rats  OR  mouse  OR  mice  OR  pig*  OR  pork  OR  beef  OR  cattle  OR  sheep  OR  lamb*  OR  chicken*  OR  calf*  OR  *horse* ))

TASK 2

You need to copy and paste the above search string into the Avanced Search tab of the Scopus Search page:

Press “Search”" button. You shoud see somethink like this:

There are over 1,000 records retrieved from the Scopus database (some look relevant and many are not, but that is always the case). On the left of the results window you can see simple filters: year, most common author names, subject areas, etc. You can explore the whole set roughly by using “Analyze search results” link above the table of the hits:

TASK 3

Next, we will export the bo=ibliometric records for more detailed bibliometric analyses in R. To do so, close the Scopus analyses window to go back to the list of records found. First, select all records by clicking box “All” in the left top of the list of references. Then click the “Export” link to the right.

A pop-up window with the export options will appear.
First, select the format of the export: we will uses .bib file (BibTex format of references, one of the standard ones).
Second, select which fields have to be exported by clicking the boxes on top of each column (or as needed).
For bibliometric analyses on the citations among papers, it is essential to tick the box next to “Include references” (i.e. data on the cited documents).

Note that, unfortunately, Scopus limits number of exported records to 2000. For longer listes of records, you will need to split them in smaller chunks for the export and then merge into a single larger dataset (not covered in this tutorial; WoS export limits are 500 records).

Click “Export” button. A file named “Scopus” (with extension matching your export type file, e.g., bib) will be saved to your downloads folder.
Note that when you export references with their reference lists included in the records, the resulting files are quite large (in our case around 16Mb).

In case you did not succeed expoerting the files (or wish to work with exactly the same ones we used, or you cannot acces Scopus), the files downloaded on 27/05/2019 are provided (the standard way os to store them in a “/data” subdirectory).

TASK 4

Create a new Rmarkdown file to save your code (you can do this within new RStudio project). Install and upload bibliometrix R package:

install.packages("bibliometrix", dependencies=TRUE) ### installs bibliometrix package and dependencies
library(bibliometrix)   #uploads the package
# Note: output not displayed for this chunk

Upload the file exported from Scopus (you can use one provided) into RStudio (note that the file path you may need to use on your computer may be different, e.g., “H:/Users/z1234567/Downloads/scopus.bib”).
Then, convert the data from that file into internal bibliometrix format.

tmp <- readFiles("data/scopus.bib")
bib <- convert2df(tmp, dbsource = "scopus", format = "bibtex") # Convert to a bibliometric data frame
#> 
#> Converting your scopus collection into a bibliographic dataframe
#> 
#> Articles extracted   100 
#> Articles extracted   200 
#> Articles extracted   300 
#> Articles extracted   400 
#> Articles extracted   500 
#> Articles extracted   600 
#> Articles extracted   700 
#> Articles extracted   800 
#> Articles extracted   900 
#> Articles extracted   1000 
#> Articles extracted   1100 
#> Articles extracted   1167 
#> Done!
#> 
#> 
#> Generating affiliation field tag AU_UN from C1:  Done!
names(bib)
#>  [1] "AU"       "TI"       "SO"       "JI"       "AB"       "DE"      
#>  [7] "ID"       "LA"       "DT"       "DT2"      "TC"       "CR"      
#> [13] "C1"       "DI"       "AR"       "RP"       "BE"       "FU"      
#> [19] "BN"       "SN"       "PN"       "PP"       "PU"       "PM"      
#> [25] "DB"       "VL"       "PY"       "AU_UN"    "AU1_UN"   "AU_UN_NR"
#> [31] "SR_FULL"  "SR"
#write.csv(bib, "data/bib_as_df.csv", row.names = FALSE) #if you want to save this data frame as a csv file

After some processing, an object called “bib” is created. It contains a data frame with each row corresponsing to one exported publication from Scopus and with each column corresponsing to a field exported from Scopus online database. (Note, if you tried to achieve this by exporting a csv file directly from Scopus, you would likely get a meessy data frame, due to missing field values shifting the cells between columns).

TASK 5

What are the contents of the columns of our “bib” data frame? Columns are labelled with a two-letter tags: AU, TI, SO, JI, AB, DE, ID, LA, DT, DT2, TC, CR, C1, DI, AR, RP, BE, FU, BN, SN, PN, PP, PU, PM, DB, VL, PY, AU_UN, AU1_UN, AU_UN_NR, SR_FULL, SR.
For a complete list and descriptions of field tags used in bibliometrix you can have a look at this file: http://www.bibliometrix.org/documents/Field_Tags_bibliometrix.pdf
Our data frame contains just a subset of these codes. Which ones?

Note that column bib$AU contains authors of each paper (as surenames and initials) separated by semicolon (;). We can easily split these strings and can extract a list of all author names to a vector:

# head(bib$AU) #have a look at the few few records on your sceen
authors <- bib$AU
authors <- unlist(strsplit(authors, ";")) #split the records into individual authors
authors <- authors[order(authors)] #order alphabetically
head(authors) #have a look again
#> [1] "ABBOTT J"       "ABE A"          "ABEDON ST"      "ABO SHEHADA M" 
#> [5] "ABOUL SOUD MAM" "ABRANTES N"
# View(unique(authors)) #use to see all the values
# write.csv(authors, "data/author_list_uncleaned.csv", row.names = FALSE) #if you want to save this data frame as a csv file

TASK 6

Cited references for each inculded paper are in the CR column of the “bib” data frame. They are in a single string, also seperated by semicolon (;). We can have a look at them and check whether familiar names were cited, e.g.:

dim(bib) #dimensions of the data frame
#> [1] 1167   32
names(bib) #names of the columns of the data frame
#>  [1] "AU"       "TI"       "SO"       "JI"       "AB"       "DE"      
#>  [7] "ID"       "LA"       "DT"       "DT2"      "TC"       "CR"      
#> [13] "C1"       "DI"       "AR"       "RP"       "BE"       "FU"      
#> [19] "BN"       "SN"       "PN"       "PP"       "PU"       "PM"      
#> [25] "DB"       "VL"       "PY"       "AU_UN"    "AU1_UN"   "AU_UN_NR"
#> [31] "SR_FULL"  "SR"
#bib$CR[1] #display a list of cited references for the first paper in the data frame 
#(we are not displaying it in this doucment as it is a very long string! - examine it on your screen instead)
#look whether some of these names are cited:
grep("NAKAGAWA, S.", bib$CR) 
#>  [1]   2   6   7  20  33  36  37  56  72  75 102 109 121 145 152 166 207
#> [18] 222 249 285 293 312 330 361 362 368 370 401 440 455 471 475 489 501
#> [35] 512 560 562 573 590 620 655 690 713 730 770
grep("CORNWELL, W.", bib$CR) 
#> [1] 15
bib[grep("CORNWELL, W.", bib$CR), c(1:3)] #who is citing?
#>                                                                                                           AU
#> MULETZ-WOLZ CR, 2019, J EVOL BIOL MULETZ WOLZ CR;BARNETT SE;DIRENZO GV;ZAMUDIO KR;TOLEDO LF;JAMES TY;LIPS KR
#>                                                                                                                                                       TI
#> MULETZ-WOLZ CR, 2019, J EVOL BIOL DIVERSE GENOTYPES OF THE AMPHIBIAN-KILLING FUNGUS PRODUCE DISTINCT PHENOTYPES THROUGH PLASTIC RESPONSES TO TEMPERATURE
#>                                                                SO
#> MULETZ-WOLZ CR, 2019, J EVOL BIOL JOURNAL OF EVOLUTIONARY BIOLOGY

Summarising bibliometric data

TASK 7

Luckily, bibliometrix package has a handy function that summarises the information contained in the “bib” data frame, so we can get some quick facts about our set of papers.

Note: this and the following tasks require quite a bit of computational power, thay may be slow or even halt on your computer.
In such case, for this exercise, make your data frame smaller by subsetting it, e.g.:
“bib <- bib[1:500, ] #taking first 500 records”. However, the results and plots you will produce with a subsetted data frame will differ from the ones presented in this document.

# Preliminary descriptive analyses 
results <- biblioAnalysis(bib, sep = ";")
summary(object = results, k = 10, pause = TRUE) 
#> 
#> 
#> Main Information about data
#> 
#>  Documents                             1167 
#>  Sources (Journals, Books, etc.)       380 
#>  Keywords Plus (ID)                    6388 
#>  Author's Keywords (DE)                3332 
#>  Period                                1980 - 2019 
#>  Average citations per documents       27 
#> 
#>  Authors                               3918 
#>  Author Appearances                    4728 
#>  Authors of single-authored documents  80 
#>  Authors of multi-authored documents   3838 
#>  Single-authored documents             84 
#> 
#>  Documents per Author                  0.298 
#>  Authors per Document                  3.36 
#>  Co-Authors per Documents              4.05 
#>  Collaboration Index                   3.54 
#>  
#>  Document types                     
#>  ARTICLE               1090 
#>  ARTICLE IN PRESS      1 
#>  BOOK CHAPTER          10 
#>  CONFERENCE PAPER      6 
#>  ERRATUM               1 
#>  LETTER                1 
#>  NOTE                  2 
#>  REVIEW                53 
#>  SHORT SURVEY          3 
#>  
#> Hit <Return> to see next table: 
#> 
#> Annual Scientific Production
#> 
#>  Year    Articles
#>     1980        1
#>     1981        2
#>     1983        1
#>     1984        1
#>     1986        2
#>     1987        2
#>     1988        4
#>     1990        3
#>     1991        2
#>     1992        3
#>     1993       11
#>     1994        8
#>     1995        8
#>     1996       10
#>     1997       17
#>     1998       20
#>     1999       14
#>     2000       20
#>     2001       22
#>     2002       19
#>     2003       29
#>     2004       28
#>     2005       29
#>     2006       46
#>     2007       37
#>     2008       42
#>     2009       44
#>     2010       53
#>     2011       66
#>     2012       74
#>     2013       90
#>     2014       98
#>     2015       87
#>     2016       73
#>     2017      100
#>     2018       73
#>     2019       28
#> 
#> Annual Percentage Growth Rate 9.698031 
#> 
#> Hit <Return> to see next table: 
#> 
#> Most Productive Authors
#> 
#>    Authors        Articles Authors        Articles Fractionalized
#> 1   POULIN R            17     POULIN R                      8.25
#> 2   MERINO S             9     ELENA SF                      3.03
#> 3   MORENO J             9     HURD H                        2.92
#> 4   SAKALUK SK           9     BENESH DP                     2.83
#> 5   RANTALA MJ           8     MORET Y                       2.62
#> 6   JOKELA J             7     ROY BA                        2.58
#> 7   SORCI G              7     TSENG M                       2.50
#> 8   ARRIERO E            6     WEBSTER JP                    2.50
#> 9   ELENA SF             6     KOELLA JC                     2.33
#> 10  HASSELQUIST D        6     TURNER PE                     2.28
#> 
#> Hit <Return> to see next table: 
#> 
#> Top manuscripts per citations
#> 
#>                                                                 Paper            TC TCperYear
#> 1  FOLSTAD I, 1992, AMERICAN NATURALIST                                        1827      67.7
#> 2  SCHULZ B, 2005, MYCOL RES                                                    719      51.4
#> 3  SCHRECK CB, 2001, AQUACULTURE                                                356      19.8
#> 4  BONNEAUD C, 2003, AM NAT                                                     345      21.6
#> 5  NORDLING D, 1998, PROC R SOC B BIOL SCI                                      306      14.6
#> 6  GUSTAFSSON L, 1994, PHILOSOPHICAL TRANSACTIONS - ROYAL SOCIETY OF LONDON, B  300      12.0
#> 7  OTS I, 1998, FUNCT ECOL                                                      286      13.6
#> 8  GARCIA DE LEANIZ C, 2007, BIOL REV                                           252      21.0
#> 9  SPRENT JI, 2007, NEW PHYTOL                                                  244      20.3
#> 10 LOVE OP, 2005, AM NAT                                                        225      16.1
#> 
#> Hit <Return> to see next table: 
#> 
#> Corresponding Author's Countries
#> 
#>           Country Articles   Freq SCP MCP MCP_Ratio
#> 1  USA                 275 0.2935 216  59     0.215
#> 2  UNITED KINGDOM      101 0.1078  64  37     0.366
#> 3  FRANCE               73 0.0779  52  21     0.288
#> 4  CANADA               52 0.0555  38  14     0.269
#> 5  GERMANY              52 0.0555  28  24     0.462
#> 6  SPAIN                50 0.0534  28  22     0.440
#> 7  FINLAND              35 0.0374  19  16     0.457
#> 8  SWEDEN               29 0.0309  17  12     0.414
#> 9  SWITZERLAND          28 0.0299  16  12     0.429
#> 10 AUSTRALIA            20 0.0213  16   4     0.200
#> 
#> 
#> SCP: Single Country Publications
#> 
#> MCP: Multiple Country Publications
#> 
#> Hit <Return> to see next table: 
#> 
#> Total Citations per Country
#> 
#>           Country      Total Citations Average Article Citations
#> 1  USA                            8121                     29.53
#> 2  UNITED KINGDOM                 4566                     45.21
#> 3  FRANCE                         2296                     31.45
#> 4  NORWAY                         2180                    155.71
#> 5  GERMANY                        1831                     35.21
#> 6  SWEDEN                         1653                     57.00
#> 7  CANADA                         1402                     26.96
#> 8  SPAIN                          1123                     22.46
#> 9  FINLAND                        1078                     30.80
#> 10 SWITZERLAND                    1031                     36.82
#> 
#> Hit <Return> to see next table: 
#> 
#> Most Relevant Sources
#> 
#>                                             Sources        Articles
#> 1  JOURNAL OF EVOLUTIONARY BIOLOGY                               41
#> 2  PROCEEDINGS OF THE ROYAL SOCIETY B: BIOLOGICAL SCIENCES       40
#> 3  EVOLUTION                                                     35
#> 4  PARASITOLOGY                                                  35
#> 5  OECOLOGIA                                                     32
#> 6  PLOS ONE                                                      30
#> 7  AMERICAN NATURALIST                                           25
#> 8  FUNCTIONAL ECOLOGY                                            24
#> 9  BMC EVOLUTIONARY BIOLOGY                                      22
#> 10 BEHAVIORAL ECOLOGY AND SOCIOBIOLOGY                           20
#> 
#> Hit <Return> to see next table: 
#> 
#> Most Relevant Keywords
#> 
#>    Author Keywords (DE)      Articles    Keywords-Plus (ID)     Articles
#> 1    PHENOTYPIC PLASTICITY         68 FEMALE                         469
#> 2    REPRODUCTION                  45 ANIMALS                        461
#> 3    FITNESS                       41 ANIMAL                         445
#> 4    REPRODUCTIVE SUCCESS          39 ARTICLE                        445
#> 5    LIFE HISTORY                  36 MALE                           407
#> 6    IMMUNITY                      31 REPRODUCTION                   365
#> 7    TRADE OFF                     31 PHYSIOLOGY                     316
#> 8    PARASITE                      28 NONHUMAN                       285
#> 9    LIFE HISTORY TRADE OFFS       27 REPRODUCTIVE SUCCESS           259
#> 10   VIRULENCE                     27 HOST PARASITE INTERACTION      239

Using summary function on bibliometrix results, we can get several screens with various tables summarising bibliometric data from our data frame - how many documents, journals, keywords, authors, publicatons timespan, collaboration index, annual publication growth rate, most prolific authors, publications per country, per journal, per keywords, etc.

You can automatically plot some of these tables (hit “return”" to displey next graph, and later you can use arrows in the top left of the plots pane to move back and forth between consecutive graphs saved in the RStudio memory):

plot(results, k = 10, pause=TRUE) #this takes top 10 values from each plottable table
#> Hit <Return> to see next plot:

#> Hit <Return> to see next plot:

#> Hit <Return> to see next plot:

#> Hit <Return> to see next plot:

#> Hit <Return> to see next plot:


#the code below is for saving these plots into a pdf:
# pdf(file = "plots/bib_descriptive_plots.pdf", height = 8, width = 8, pointsize=10) #
# plot(results, k = 20, pause=FALSE) #this takes top 20 values from each plottable table
# dev.off()

TASK 8

The cited papers from the CR field of the data frame can be analysed using function citations.
Function citations makes it easy to generate the frequency tables of the most cited papers or the most cited first authors from the reference lists of our papers downloaded from Scopus.

Ten most cited papers:

mostcitedP <- citations(bib, field = "article", sep = ";")
cbind(mostcitedP$Cited[1:10])
#>                                                                                                                                                                [,1]
#> LOCHMILLER, R.L., DEERENBERG, C., TRADE-OFFS IN EVOLUTIONARY IMMUNOLOGY: JUST WHAT IS THE COST OF IMMUNITY? (2000) OIKOS, 88, PP. 87-98                          45
#> HAMILTON, W.D., ZUK, M., HERITABLE TRUE FITNESS AND BRIGHT BIRDS: A ROLE FOR PARASITES? (1982) SCIENCE, 218, PP. 384-387                                         31
#> MORET, Y., SCHMID-HEMPEL, P., SURVIVAL FOR IMMUNITY: THE PRICE OF IMMUNE SYSTEM ACTIVATION FOR BUMBLEBEE WORKERS (2000) SCIENCE, 290, PP. 1166-1168              30
#> FORBES, M.R.L., PARASITISM AND HOST REPRODUCTIVE EFFORT (1993) OIKOS, 67, PP. 444-450                                                                            29
#> STEARNS, S.C., (1992) THE EVOLUTION OF LIFE HISTORIES, , OXFORD UNIVERSITY PRESS, OXFORD                                                                         25
#> MINCHELLA, D.J., HOST LIFE-HISTORY VARIATION IN RESPONSE TO PARASITISM (1985) PARASITOLOGY, 90, PP. 205-216                                                      24
#> SHELDON, B.C., VERHULST, S., ECOLOGICAL IMMUNOLOGY: COSTLY PARASITE DEFENCES AND TRADE-OFFS IN EVOLUTIONARY ECOLOGY (1996) TRENDS ECOL. EVOL., 11, PP. 317-321   19
#> ROLFF, J., SIVA-JOTHY, M.T., INVERTEBRATE ECOLOGICAL IMMUNOLOGY (2003) SCIENCE, 301, PP. 472-475                                                                 18
#> ANDERSON, R.M., MAY, R.M., COEVOLUTION OF HOSTS AND PARASITES (1982) PARASITOLOGY, 85, PP. 411-426                                                               17
#> FRANK, S.A., MODELS OF PARASITE VIRULENCE (1996) Q. REV. BIOL., 71, PP. 37-78                                                                                    17

Ten most cited authors:

mostcitedA <- citations(bib, field = "author", sep = ";")
cbind(mostcitedA$Cited[1:10])
#>                 [,1]
#> WINGFIELD J C    425
#> POULIN R         410
#> MLLER A P        394
#> SCHMID HEMPEL P  329
#> HASSELQUIST D    326
#> READ A F         306
#> ZUK M            281
#> EBERT D          275
#> SHELDON B C      263
#> BENSCH S         229

The function localCitations generates the frequency table of the locally most cited authors. Locally, means that only citations are counted only within the given data set - i.e. how many times an author/paper that is in this data set has been cited by other authors/papers also in the data set.

Ten most frequent local cited authors and papers:

mostcitedLA <- localCitations(bib, results, sep = ";")
#> Articles analysed   100 
#> Articles analysed   200 
#> Articles analysed   300 
#> Articles analysed   400 
#> Articles analysed   500 
#> Articles analysed   600 
#> Articles analysed   700 
#> Articles analysed   800
#> Error in grep(y, M$CR[M$PY >= Year]): invalid regular expression '\(2015\) CHANGES IN PHYTOHAEMAGGLUTININ SKIN-SWELLING RESPONSES DURING THE BREEDING SEASON IN A MULTI-BROODED SPECIES, THE EURASIAN TREE SPARROW: DO MALES WITH HIGHER TESTOSTERONE LEVELS SHOW STRONGER IMMUNE RESPONSES? [UNTERSCHIEDLICHE IMMUNANTWORTEN ANHAND PHYTOHAEMAGGLUTININ-HAUTSCHWELLUNG BEI FELDSPERLINGEN WHREND DER BRUTZEIT: ZEIGEN MNNCHEN MIT HHEREN TESTOSTERONWERTEN STRKERE IMMUNANTWORTEN?]', reason 'Invalid character range'
mostcitedLA[1:10]
#> Error in eval(expr, envir, enclos): object 'mostcitedLA' not found

Creating bibliometric networks

So far, we looked only at the numbers - who or what gets cited most, either from the main papers list or from the lists of the references within these papers. Now it is time to look at the actual networks of citations and also other types of networks that can be created using our data set.

To do so we will be creating various rectangular matrices which reflect connections of different attributes of Papers/Authors. These matrices than can be plotted as bipartite networks and analysesd.

Co-citation or coupling networks are a special type of newtorks resulting from scientific papers containing references to other scientific papers.

Package bibliometrix contains function biblioNetwork which makes creating bibliomgraphic networks easy. This function can create the most frequently used coupling networks: Authors, Sources, and Countries.

TASK 9

Bibliographic coupling - two articles are bibliographically coupled if they share at leas one reference from their reference lists (i.e. at least one cited source appears in the reference lists/bibliographies of both papers (Kessler, 1963).

NetMatrix <- biblioNetwork(bib, analysis = "coupling", network = "references", sep = ";")
net = networkPlot(NetMatrix, weighted = NULL, n = 10, Title = "Papers' bibliographic coupling", type = "fruchterman", size = 5, remove.multiple = TRUE, labelsize = 0.5)

Above, we plotted only the top 10 most coupled papers (n=10), try increasing this number to 100 (would not recommend further increasing the number of displayed nodes - it gets slow and messy).
What happens and why?

TASK 10

Author’s bibliographic coupling - two authors are bibliographically coupled if they share at leas one reference form their reference lists.

NetMatrix <- biblioNetwork(bib, analysis = "coupling", network = "authors", sep = ";")
net = networkPlot(NetMatrix, weighted = NULL, n = 10, Title = "Authors' bibliographic coupling", type = "fruchterman", size = 5, remove.multiple = TRUE, labelsize = 0.8)

Above, we plotted only the top 10 most coupled authors (n=10), try increasing this number to 100 (would not recommend further increasing the number of displayed nodes to >50 - it gets slow and messy).
What happens and why?

TASK 11

Bibliographic co-citation is kind of opposite to bibliographic coupling, in so that two papers are linked by co-citatio when both are cited in a third papers.

NetMatrix <- biblioNetwork(bib[1:50,], analysis = "co-citation", network = "references", sep = ";")
net = networkPlot(NetMatrix, weighted=NULL, n = 10, Title = "Papers' co-citations", type = "fruchterman", size = 5, remove.multiple = TRUE, labelsize = 0.5)

Note that for creating this matrix we only used first 50 papers from our data set - this is because the resulting matrix is a matrix of ALL cited papers and it gets HUGE). Also, we plotted only the top 10 most coupled papers (n=10), try increasing this number to 20 (would not recommend further increasing the number of displayed nodes to >50 - it gets slow and messy).
What happens and why?

TASK 12

Bibliographic collaboration is a network where nodes are authors and links are co-authorships on the papers.

NetMatrix <- biblioNetwork(bib, analysis = "collaboration", network = "authors", sep = ";")
net = networkPlot(NetMatrix, weighted = NULL, n = 10, Title = "Authors' collaborations", type = "fruchterman", size = 5, remove.multiple = TRUE, labelsize = 0.5)

Above, we plotted only the top 10 most collaborating authors (n=10), try increasing this number to 100 (would not recommend further increasing the number of displayed nodes - it gets slow and messy).
What happens and why?

TASK 13

Country Scientific Collaboration - we can visualise authors from which countries publish papers together most frequently.

bib <- metaTagExtraction(bib, Field = "AU_CO", sep = ";") #we need to extract countries from the affiliations first
NetMatrix <- biblioNetwork(bib, analysis = "collaboration", network = "countries", sep = ";")
net = networkPlot(NetMatrix, n = 10, Title = "Country Collaboration", type = "auto", size = TRUE, remove.multiple = FALSE, labelsize = 0.5)

Above, we plotted only the top 10 most collaborating countrie (n=10), try increasing this number to 50 (would not recommend further increasing the number of displayed nodes to >100 - it gets slow and messy).
What happens and why?

TASK 14

Keyword co-occurrences - we can also visualise which papers share most keywords (from Scopus database).

NetMatrix <- biblioNetwork(bib, analysis = "co-occurrences", network = "keywords", sep = ";")
net = networkPlot(NetMatrix, n = 50, Title = "Keyword co-occurance", type = "fruchterman", size = T, remove.multiple = FALSE, labelsize = 0.7, edgesize = 5)

Try replacing network = “keywords” with network = “author_keywords” and see what happens. You can also try to display fewer/more keywords in the plot.

TASK 15

Note: you may want to skip this step on a big data set or a slow computer.

Co-Word Analysis - uses the word co-occurrences in a bibliographic collection to map the conceptual structure of research. It works via a separate function conceptualStructure that creates a conceptual structure map of a scientific field performing Correspondence Analysis (CA), Multiple Correspondence Analysis (MCA) or Metric Multidimensional Scaling (MDS) and Clustering of a bipartite network of terms extracted from keyword, title or abstract fields of the data frame.

CS <- conceptualStructure(bib, field = "ID", minDegree = 20, k.max = 5, stemming = FALSE, labelsize = 10)

The code above uses field ID, which stands for “conceptualStructure”. You coul try using authors keywords, “DE” field, instead.
Is the map different?

TASK 15

Note: you may want to skip this step on a big data set or a slow computer.
Historical Direct Citation Network - represents a chronological network map of most relevant direct citations in a bibliographic collection, i.e who is citing whom and in what order. histNetwork function calculates a chronological direct citation network matrix which then is plotted using histPlot:

#options(width = 130)
histResults <- histNetwork(bib, min.citations = 10, sep = ";")
#> Articles analysed   100 
#> Articles analysed   200 
#> Articles analysed   300 
#> Articles analysed   400 
#> Articles analysed   500 
#> Articles analysed   600 
#> Articles analysed   656
net = histPlot(histResults, labelsize = 2, arrowsize = 0.5)

#> 
#>  Legend
#> 
#>                                                                                  Paper
#> 1992 - 15                                         FOLSTAD I, 1992, AMERICAN NATURALIST
#> 1994 - 26  GUSTAFSSON L, 1994, PHILOSOPHICAL TRANSACTIONS - ROYAL SOCIETY OF LONDON, B
#> 1997 - 62                                                 SIIKAMKI P, 1997, FUNCT ECOL
#> 1997 - 63                                                 ALLANDER K, 1997, FUNCT ECOL
#> 1998 - 72                                      NORDLING D, 1998, PROC R SOC B BIOL SCI
#> 2000 - 100                                      ILMONEN P, 2000, PROC R SOC B BIOL SCI
#> 2000 - 110                                                 WORDEN BD, 2000, ANIM BEHAV
#> 2002 - 143                                                       AHMED AM, 2002, OIKOS
#> 2003 - 167                                                    BONNEAUD C, 2003, AM NAT
#> 2004 - 194                                                    JACOT A, 2004, EVOLUTION
#> 2004 - 199                                                 BONNEAUD C, 2004, EVOLUTION
#> 2005 - 217                                     CHADWICK W, 2005, PROC R SOC B BIOL SCI
#> 2005 - 219                                                   MARZAL A, 2005, OECOLOGIA
#> 2006 - 236                                                   ULLER T, 2006, FUNCT ECOL
#> 2006 - 246                                      VELANDO A, 2006, PROC R SOC B BIOL SCI
#> 2007 - 294                                                 BENSCH S, 2007, J ANIM ECOL
#> 2008 - 308                                                 MARZAL A, 2008, J EVOL BIOL
#> 2009 - 346                                               KNOWLES SCL, 2009, FUNCT ECOL
#> 2010 - 358                                              KIVLENIECE I, 2010, ANIM BEHAV
#> 2010 - 387                                              KNOWLES SCL, 2010, J EVOL BIOL
#>                                           DOI Year LCS  GCS
#> 1992 - 15                      10.1086/285346 1992  35 1827
#> 1994 - 26              10.1098/RSTB.1994.0149 1994  26  300
#> 1997 - 62    10.1046/J.1365-2435.1997.00075.X 1997  14   47
#> 1997 - 63    10.1046/J.1365-2435.1997.00095.X 1997  14   66
#> 1998 - 72              10.1098/RSPB.1998.0432 1998  31  306
#> 2000 - 100             10.1098/RSPB.2000.1053 2000  17  203
#> 2000 - 110             10.1006/ANBE.1999.1368 2000  11   53
#> 2002 - 143  10.1034/J.1600-0706.2002.970307.X 2002  14  109
#> 2003 - 167                     10.1086/346134 2003  34  345
#> 2004 - 194 10.1111/J.0014-3820.2004.TB01603.X 2004  17  105
#> 2004 - 199 10.1111/J.0014-3820.2004.TB01633.X 2004  20  119
#> 2005 - 217             10.1098/RSPB.2004.2959 2005  12   63
#> 2005 - 219          10.1007/S00442-004-1757-2 2005  21  215
#> 2006 - 236   10.1111/J.1365-2435.2006.01163.X 2006  11   63
#> 2006 - 246             10.1098/RSPB.2006.3480 2006  16  164
#> 2007 - 294   10.1111/J.1365-2656.2006.01176.X 2007  12  151
#> 2008 - 308   10.1111/J.1420-9101.2008.01545.X 2008  15  137
#> 2009 - 346   10.1111/J.1365-2435.2008.01507.X 2009  16  123
#> 2010 - 358      10.1016/J.ANBEHAV.2010.09.004 2010  11   39
#> 2010 - 387   10.1111/J.1420-9101.2009.01920.X 2010  13  136

Only articles with minimum of 10 citations are included in teh above analysis, if you change this number to a higher value, the analyses will be quicker and the plot less dense.

MORE TO DO You can use different types of network plots - just tweak “type” parameter in the networkPlot function (check the vignette for the available options). Type indicates the network map layout: circle, kamada-kawai, mds, etc.

You can use non-R tools to visualise bibliographic networks, e.g. VOSviewer software by Nees Jan van Eck and Ludo Waltman (http://www.vosviewer.com). When in R function you usetype=“vosviewer”, the function will export the network a standard “pajek” network file (named “vosnetwork.net”), which can be used in other network-plotting software, including VOSviewer.

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